HEART DISEASE PREDICTION SYSTEM USING NEIGHBORING DISTANCE BASED OUTLIER DETECTION APPROACH
Author(s):
D.Priyanth
Keywords:
Heart disease, outlier data, XGBoost, Accuracy, machine learning.
Abstract
The clinical or hospital information is stored in large volume in medical database need intelligent based discovery. Medical data is huge and require more possibilities analysis. Data mining the procedure to analyze a huge volume of available medical data from various futuristic potential and provides knowledge information to the physician to predict the patient disease accurately. Existing system used a heart disease prediction model (HDPM) approach for Clinical Decision Support System (CDSS). This was a web-based application. It will collect the data of patients and the information sent to Heart Disease Clinical Decision Support System. This was designed for medical practitioners. The disadvantage of system is it works on limited data set and accuracy is also low. The propose uses neighboring distance-based outlier detection approach. It can give better results in terms of accuracy. This can help patients in getting a quick diagnosis with a lot less cost.
Article Details
Unique Paper ID: 154439

Publication Volume & Issue: Volume 8, Issue 11

Page(s): 159 - 164
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